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import os
import random
import numpy as np
import pandas as pd
import networkx as nx
import matplotlib.pyplot as plt
# set up the network
def setup_network(filename):
# load the dataset
df = pd.read_excel(filename, header=None)
investors = df.iloc[:, 0].tolist()
# create a graph
G = nx.Graph()
# add nodes to the graph
G.add_nodes_from(investors)
# add edges to the graph
for i in range(len(investors)):
# get the name of investor_i
investor_i = investors[i]
# get the list of companies invested by investor_i
invested_list_i = df.iloc[i, 1:].dropna().tolist()
# compare this investor with the rest of investors
for j in range(i+1, len(investors)):
# get the name of investor_j
investor_j = investors[j]
# get the list of companies invested by investor_j
invested_list_j = df.iloc[j, 1:].dropna().tolist()
# compute the intersection of the two lists
intersection_set = set(invested_list_i).intersection(set(invested_list_j))
# if the intersection is not empty, add an edge between the two investors
# set the weight of the edge to be the number of common companies they invested together
if len(intersection_set) > 0:
G.add_edge(investor_i, investor_j, weight=len(intersection_set))
# extract the largest connected component
largest_connected_component = max(nx.connected_components(G), key=len)
# create a subgraph of the largest connected component
subgraph = G.subgraph(largest_connected_component)
return subgraph
# plot the network
def plot_network(G, title):
# plot the graph
plt.figure(figsize=(20, 20))
pos = nx.spring_layout(
G,
weight='weight',
k=6,
iterations=100
)
nx.draw(
G, pos,
with_labels=True,
node_color='lightblue',
node_size=200,
width=0.15,
font_size=10,
font_weight='light'
)
labels = nx.get_edge_attributes(G, 'weight')
nx.draw_networkx_edge_labels(G, pos, edge_labels=labels, font_size=5, alpha=0.5)
plt.title(title)
plt.show()
whole_network = setup_network('./Assignment_Data/2019_Q1.xlsx')
plot_network(whole_network,'2019_Q1 Network')
